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1. Using a Hybrid of Argumentation and Problem Solving Prompts to Facilitate Undergraduates' Problem Solving Performance and Confidence

   Rebello, C.M. ( January 2019 , The 13th Conference of the European Science Education Research Association (ESERA))

   Educational reform documents in the U.S. promote the incorporation of argumentation in science and engineering as a 21st century learning skill. Our aim was to infuse argumentation in a calculus-based physics course for future scientists and engineers. We conducted a study in a problem solving recitation session of the course. All students were asked to solve the same problem and were provided prompts to structure their solutions. The intervention condition was required to justify their solution procedure prior to solving the problem. Results showed that the intervention condition had a greater proportion of correct solutions, higher confidence in their approach, and were more likely to suggest alternative approaches to solving the problem than students in the control condition. 

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2. Engineering Students' Epistemic Cognition in the Context of Problem Solving: Epistemic Cognition in Problem Solving

   https://doi.org/10.1002/jee.20183  

   Faber, Courtney ; Benson, Lisa C. ( October 2017 , Journal of Engineering Education)
3. Student difficulties during structure-function problem solving: Structure-Function Problem Solving

   https://doi.org/10.1002/bmb.21166  

   Halmo, Stephanie M. ; Sensibaugh, Cheryl A. ; Bhatia, Kush S. ; Howell, Alexandra ; Ferryanto, Ersta P. ; Choe, Bryant ; Kehoe, Kaitlin ; Watson, Morgan ; Lemons, Paula P. ( October 2018 , Biochemistry and Molecular Biology Education)
4. Perspectives of Engineering Faculty and Practitioners on Creative Problem Solving in Solving Ill-Structured Problems

   Akinci, S. ; Cetin, K.S. ; Ahn, B. ( April 2022 , ASEE Annual Conference proceedings)

   Creativity plays an important role in engineering problem solving, particularly when solving an ill-structured problem, and has been a topic of increasing research interest in recent years. Prior research on creativity has been conducted in problem solving settings, predominantly focusing on undergraduate engineering students, including how faculty can foster creativity in engineering students, how engineering faculty perceive their students’ creativity, and how to measure it. However, more work is needed to examine engineering faculty and practitioner perspectives on the role of creativity when they solve an engineering problem themselves. Since engineering students learn problem solving, at least initially, mainly from their professors, it is essential to understand how faculty perceive their own creativity in problem solving. Similarly, given that practitioners solve ill-structured engineering problems on a regular basis in the workplace and that most of the students go on to work in the engineering industry when they graduate and ultimately become practitioners, it is also important to explore practitioner perspectives on creativity in problem solving settings. As part of an ongoing NSF-funded study, this paper investigates how engineering faculty’s and practitioners’ creativity influences their problem solving processes, how their perspectives on creativity in a problem solving environment differ, and what factors impact their creativity. Five tenure-track faculty in civil engineering and five practitioners were interviewed after they solved an ill-structured engineering problem. Participants’ responses were transcribed and coded using initial coding. This paper discusses their responses to semi-structured interview questions. The findings suggest that faculty and practitioners feel more creative when they are familiar with the subject area of a problem. If they are aware of a particular solution that has been developed and used before or have access to resources to look them up, they may not necessarily embrace creativity. The findings indicated differences not only across faculty and practitioners but also within the faculty and practitioner participants. Similarities and differences between faculty and practitioners in creative problem solving and the themes emerged are discussed and recommendations for educators are provided. 

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5. Evaluation of Direct Collocation Optimal Control Problem Formulations for Solving the Muscle Redundancy Problem

   https://doi.org/10.1007/s10439-016-1591-9  

   De Groote, Friedl ; Kinney, Allison L. ; Rao, Anil V. ; Fregly, Benjamin J. ( January 2016 , Annals of Biomedical Engineering)

   Estimation of muscle forces during motion involves solving an indeterminate problem (more unknown muscle forces than joint moment constraints), frequently via optimization methods. When the dynamics of muscle activation and contraction are modeled for consistency with muscle physiology, the resulting optimization problem is dynamic and challenging to solve. This study sought to identify a robust and computationally efficient formulation for solving these dynamic optimization problems using direct collocation optimal control methods. Four problem formulations were investigated for walking based on both a two and three dimensional model. Formulations differed in the use of either an explicit or implicit representation of contraction dynamics with either muscle length or tendon force as a state variable. The implicit representations introduced additional controls defined as the time derivatives of the states, allowing the nonlinear equations describing contraction dynamics to be imposed as algebraic path constraints, simplifying their evaluation. Problem formulation affected computational speed and robustness to the initial guess. The formulation that used explicit contraction dynamics with muscle length as a state failed to converge in most cases. In contrast, the two formulations that used implicit contraction dynamics converged to an optimal solution in all cases for all initial guesses, with tendon force as a state generally being the fastest. Future work should focus on comparing the present approach to other approaches for computing muscle forces. The present approach lacks some of the major limitations of established methods such as static optimization and computed muscle control while remaining computationally efficient. 

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